Interpretive Summary: Dew and other forms of leaf wetness can introduce potential errors into the remote sensing so soil moisture when the satellites and aircraft involved have an early morning overpass. There is currently no methodology to monitor dew amount automatically which is a necessity for any type of mitigation algorithm to be developed on a large scale. Electrical resistance leaf wetness sensors have proven in the past to accurately determine dew presence, but not amount. The Soil Moisture Experiments in 2005 (SMEX05) provided a unique opportunity to include dew monitoring as a element in soil moisture remote sensing. Coupled with physical sampling of dew, electrical resistance sensors were deployed throughout the study region. Both corn and soybeans were monitored and errors in dew estimation were approximately 0.05 kg/m2/(Leaf Area Index), which is within the bounds of acceptable errors for soil moisture remote sensing. Future work in remote sensing as well as hydrologic modeling and plant pathology will benefit from this new found ability to estimate surface leaf wetness.

Technical Abstract:
Estimating the amount of water on leaf surfaces is an increasing concern for remote sensing and hydrology. Measuring the magnitude and spatial extent of leaf wetness events will provide useful information for water and energy balance modeling and remote sensing. As part of the Soil Moisture Experiments 2005 (SMEX05), the temporal and spatial characterization of leaf wetness over a heterogeneous agricultural domain was investigated. Leaf wetness sensors and physical measurements were collected from June 15 to July 3, 2005 in and around the Walnut Creek Watershed near Ames, Iowa, U.S.A. Comparison of the results of the in situ leaf wetness sensor measurements and the physical sampling revealed a moderate correlation for both corn (Zea mays L.)and soybeans (Glycine max Merr.). Regression equations were developed to estimate leaf wetness quantity from these leaf wetness sensors and combined with a vegetation leaf area index map to produce a spatial leaf wetness product hourly during the experiment with an error of approximately 0.05 kg/m2/LAI. Using this strategy, future efforts in spatial hydrologic modeling and remote sensing would be able to incorporate quantitative estimates of leaf wetness amount in watershed scale studies using only in situ measurements.